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Natural Language Processing with TensorFlow

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Introduction to Natural Language Processing

Introduction: Natural Language Processing What Is Natural Language Processing?Tasks of Natural Language Processing The Traditional Approach to Natural Language Processing The Deep Learning Approach to Natural Language Processing Summary: Introduction to Natural Language Processing

Understanding TensorFlow 2

Introduction: Understanding TensorFlow 2 What Is TensorFlow 2?TensorFlow 2 Architecture: Building and Executing Graphs Café Le TensorFlow 2: Understanding TensorFlow 2 with an Analogy Flashback: TensorFlow 1 Defining Inputs in TensorFlow Building a Data Pipeline Using the tf.data API Defining Variables and Output in TensorFlow Defining Operations in TensorFlow Neural Network-Related Operations Keras: TensorFlow's Model-Building API Implementing Our First Neural Network Summary: Understanding TensorFlow 2 Quiz: Understanding TensorFlow 2

Word2vec: Learning Word Embeddings

Introduction of Word2vec: Learning Word Embeddings Classical Approaches to Learning Word Representations An Intuitive Understanding of Word2vec The Skip-Gram Algorithm Data Generators in the Skip-Gram Algorithm with TensorFlow Implementing the Skip-Gram Architecture with TensorFlow Training and Evaluating the Skip-Gram Model The Continuous Bag-of-Words Algorithm Summary of Word2vec: Learning Word Embeddings Quiz of Word2vec: Learning Word Embeddings

Advanced Word Vector Algorithms

Introduction: Advanced Word Vector Algorithms

GloVe: Global Vectors Representation

Implementing GloVe

Generating Data for GloVe

Training and Evaluating GloVe

ELMo: Taking Ambiguities Out of Word Vectors

Preparing Inputs for ELMo

Generating Embeddings with ELMo

Dataset in Document Classification with ELMo

Generating Document Embeddings

Classifying Documents with Document Embeddings

Summary: Advanced Word Vector Algorithms

Quiz: Advanced Word Vector Algorithms

Sentence Classification with Convolutional Neural Networks

Introduction: Sentence Classification with CNNs Introducing CNNs Understanding CNNs: Convolution Operations Understanding CNNs: Pooling Operations Understanding CNNs: Fully Connected Layers Exercise: Image Classification on Fashion-MNIST with CNN CNNs for Sentence Classification: Transformation of Data CNNs for Sentence Classification: Downloading and Preparing Data CNNs for Sentence Classification: Building a Tokenizer The Sentence Classification CNN Model Sentence Classification with CNNs Summary: Sentence Classification with CNNs Quiz: Sentence Classification with CNNs

Recurrent Neural Networks

Introduction: Recurrent Neural Networks Understanding RNNs Backpropagation through Time Applications of RNNs Named Entity Recognition with RNNs: Preparing Data Named Entity Recognition with RNNs: Defining the Model Named Entity Recognition with RNNs: Training and Evaluation NER with Character and Token Embeddings Summary: Recurrent Neural Networks Quiz: Recurrent Neural Networks

Understanding Long Short-Term Memory Networks

Introduction: Understanding Long Short-Term Memory Networks Understanding Long Short-Term Memory Networks How LSTMs Solve the Vanishing Gradient Problem Improving LSTMs Other Variants of LSTMs Summary: Understanding Long Short-Term Memory Networks Quiz: Understanding Long Short-Term Memory Networks

Applications of LSTM: Generating Text

Introduction: Applications of LSTMs—Generating Text Understanding the Data Implementing the Language Model Generating New Text with the Model Comparing LSTMs to LSTMs with Peephole Connections and GRUs Improving Sequential Models: Beam Search Improving LSTMs: Generating Text with Words Instead of N-grams Summary: Applications of LSTMs—Generating Text Quiz: Applications of LSTMs—Generating Text

Sequence-to-Sequence Learning: Neural Machine Translation

Introduction: Sequence-to-Sequence Learning—NMT Machine Translation Understanding Neural Machine Translation Preparing Data for the NMT System Defining the NMT Model Attention: Analyzing, Computing, and Implementing Training the NMT The BLEU Score: Evaluating Machine Translation Systems Visualizing Attention Patterns Inference with NMT Other Applications of Seq2Seq Models: Chatbots Summary: Sequence-to-Sequence Learning—NMT Quiz: Sequence-to-Sequence Learning—NMT

Transformers

Introduction: Transformers Transformer Architecture: Encoder, Decoder, and Computing Output Transformer Architecture: Embedding Layers Transformer Architecture: Residuals and Normalization Understanding BERT Use Case: Using BERT to Answer Questions Use Case: Implementing BERT Training and Evaluating Model and Answering Questions with BERT Summary: Transformers Quiz: Transformers

Image Captioning with Transformers

Introduction: Image Captioning with Transformers Getting to Know the Data Processing and Tokenizing Data Defining a tf.data.Dataset The Machine Learning Pipeline for Image Caption Generation Implementing and Training the Model with TensorFlow Evaluating the Results Quantitatively Evaluating the Model and Generating Captions from It Summary: Image Captioning with Transformers Quiz: Image Captioning with Transformers

Final Remarks

Appendix: Mathematical Foundations and Advanced TensorFlow

Introduction to the Technical Tools Basic Data Structures Special Types of Matrices Probability Visualizing Word Embeddings with TensorBoard Summary: Appendix

Preparing Inputs for ELMo

Learn to prepare the inputs for ELMo embeddings.

We'll cover the following

The format_text_for_elmo() function

Here, we’ll define a function that will convert a given list of strings to the format ELMo expects the inputs to be in. Remember that we set the signature of ELMo to be tokens. An example input to the signature tokens would look as follows.

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